Rechargeable Sensor Activation under Temporally Correlated Events

Wireless sensor networks are often deployed to detect "interesting events" that are bound to show some degree of temporal correlation across their occurrences. Typically, sensors are heavily constrained in terms of energy, and thus energy usage at the sensors must be optimized for efficient operation of the sensor system. A key optimization question in such systems is - how the sensor (assumed to be rechargeable) should be activated in time so that the number of interesting events detected is maximized under the typical slow rate of recharge of the sensor. In this paper, we consider the activation question for a single sensor, and pose it in a stochastic decision framework. The recharge-discharge dynamics of a rechargeable sensor node, along with temporal correlations in the event occurrences makes the optimal sensor activation question very challenging. Under complete state observability, we outline the structure of a class of deterministic, memoryless policies that approach optimality as the energy bucket size at the sensor becomes large; in addition, we provide an activation policy which achieves the same asymptotic performance but does not require the sensor to keep track of its current energy level. For the more practical scenario, where the inactive sensor may not have complete information about the state of event occurrences in the system, we outline the structure of the deterministic, history-dependent optimal policy. We then develop a simple, deterministic, memoryless activation policy based upon energy balance and show that this policy achieves near optimal performance under certain realistic assumptions.